
# Importing and installing libaries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt 
!pip install seaborn 
import seaborn as sns
!pip install plotly==4.14.3
import plotly.graph_objects as go
import warnings

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# Importing Datasets 
Data_2018 = pd.read_excel("/Users/Admin/Desktop/India Startup Funding/startup_funding2018.xls")
Data_2019 = pd.read_csv("/Users/Admin/Desktop/India Startup Funding/startup_funding2019.csv")
Data_2020 = pd.read_csv("/Users/Admin/Desktop/India Startup Funding/startup_funding2020.csv")
Data_2021 = pd.read_csv("/Users/Admin/Desktop/India Startup Funding/startup_funding2021.csv")


# Adding a new column called Funding Year to all datasets
Data_2018["Funding Year"] = 2018
Data_2019["Funding Year"] = 2019
Data_2020["Funding Year"] = 2020
Data_2021["Funding Year"] = 2021


# Previewing 2018 dataset 
Data_2018.head()


# Printing a concise summary of the 2018 dataset
Data_2018.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 526 entries, 0 to 525
Data columns (total 9 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Company Name   526 non-null    object 
 1   Industry       526 non-null    object 
 2   Round/Series   526 non-null    object 
 3   Amount         526 non-null    object 
 4   Location       526 non-null    object 
 5   About Company  526 non-null    object 
 6   Calculation    1 non-null      float64
 7   Amount.1       291 non-null    object 
 8   Funding Year   526 non-null    int64  
dtypes: float64(1), int64(1), object(7)
memory usage: 37.1+ KB


# Renaming columns in 2018 data set 
Data_2018.rename(columns = {"Company Name": "Company/Brand", "Industry": "Sector", "Round/Series": "Stage", "Amount": "Amount($)", "Location": "HeadQuarter", "About Company": "What it does"}, inplace = True)
Data_2018.head()


#Next step is to drop columns that are irrelevant to answering my research questions.
# Dropping columns
Data_2018.drop(columns = ["Calculation", "Amount.1", "What it does", "Stage"], inplace = True)
Data_2018.head()


#Renaming Column name Amount($) to Amount in USD 
Data_2018.rename(columns = {"Amount($)": "Amount in USD"}, inplace = True)


# Changing amount datatype to int/float 
Data_2018["Amount in USD"] = pd.to_numeric(Data_2018["Amount in USD"], errors = "coerce").fillna(0, downcast = "infer")
Data_2018.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 526 entries, 0 to 525
Data columns (total 5 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Company/Brand  526 non-null    object 
 1   Sector         526 non-null    object 
 2   Amount in USD  526 non-null    float64
 3   HeadQuarter    526 non-null    object 
 4   Funding Year   526 non-null    int64  
dtypes: float64(1), int64(1), object(3)
memory usage: 20.7+ KB


# Checking for any other errors in the 2018 data set 
Data_2018.isnull().sum()

Company/Brand    0
Sector           0
Amount in USD    0
HeadQuarter      0
Funding Year     0
dtype: int64


# Stripping data to separate the values in the column by commas and select the first value only
Data_2018['Sector'] = Data_2018['Sector'].str.split(',').str[0] 
Data_2018['HeadQuarter'] = Data_2018['HeadQuarter'].str.split(',').str[0]
Data_2018


# Replacing '-' values in the sector and headquarter columns with 'unknown'
Data_2018["HeadQuarter"].replace('-', value = "unknown", inplace = True)
Data_2018["Sector"].replace('-', value = "unknown", inplace = True)
Data_2018


Data_2018


# Previewing 2019 dataset to view 
Data_2019.head()


# Printing a summary of the dataset to find errors and missing values. 
Data_2019.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 89 entries, 0 to 88
Data columns (total 10 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Company/Brand  89 non-null     object 
 1   Founded        60 non-null     float64
 2   HeadQuarter    70 non-null     object 
 3   Sector         84 non-null     object 
 4   What it does   89 non-null     object 
 5   Founders       86 non-null     object 
 6   Investor       89 non-null     object 
 7   Amount($)      89 non-null     object 
 8   Stage          43 non-null     object 
 9   Funding Year   89 non-null     int64  
dtypes: float64(1), int64(1), object(8)
memory usage: 7.1+ KB


# Dropping Founders, Stage and What it does columns.
Data_2019.drop(columns = ["Founders", "Stage", "What it does"], inplace = True)
Data_2019.head()


#Renaming Column name Amount($) to Amount in USD 
Data_2019.rename(columns = {"Amount($)": "Amount in USD"}, inplace = True)


# Removing dollar sign from the Amount in USD column 
Data_2019["Amount in USD"] = Data_2019["Amount in USD"].replace({"\$": "", ",": ""}, regex = True)


# Changing amount datatype to int/float 
Data_2019["Amount in USD"] = pd.to_numeric(Data_2019["Amount in USD"], errors = "coerce").fillna(0, downcast = "infer")


# Changing undisclosed values in 'Amount in USD' to 0
Updated = Data_2019["Amount in USD"] == "Undisclosed"
Data_2019.loc[Updated, "Amount in USD"] = 0
Data_2019


# Replacing null values in columns with unknown
Data_2019["Founded"].replace(np.NaN, value = "unknown", inplace = True)
Data_2019["HeadQuarter"].replace(np.NaN, value = "unknown", inplace = True)
Data_2019["Sector"].replace(np.NaN, value = "unknown", inplace = True)


#Changing datatype for Founded Column from object to int
Data_2019['Founded']= Data_2019['Founded'].astype('str')


# Checking for null values in 2019 Dataset.
Data_2019.isnull().sum()

Company/Brand    0
Founded          0
HeadQuarter      0
Sector           0
Investor         0
Amount in USD    0
Funding Year     0
dtype: int64


Data_2019


# Previewing 2020 dataset
Data_2020.head()


# Printing a concise summary of 2020 Dataset. 
Data_2020.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1055 entries, 0 to 1054
Data columns (total 11 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   Company/Brand  1055 non-null   object
 1   Founded        843 non-null    object
 2   HeadQuarter    961 non-null    object
 3   Sector         1042 non-null   object
 4   What it does   1055 non-null   object
 5   Founders       1043 non-null   object
 6   Investor       1017 non-null   object
 7   Amount($)      1052 non-null   object
 8   Stage          591 non-null    object
 9   Unnamed: 9     2 non-null      object
 10  Funding Year   1055 non-null   int64 
dtypes: int64(1), object(10)
memory usage: 90.8+ KB


# Dropping Columns Founders, Stage, What it does and Unnamed: 9
Data_2020.drop(columns = ["Founders", "Stage", "What it does", "Unnamed: 9"], inplace = True)


# Viewing the columns and rows of the 2020 dataset. 
Data_2020.head()


#Renaming Column name Amount($) to Amount in USD 
Data_2020.rename(columns = {"Amount($)": "Amount in USD"}, inplace = True)


# Removing dollar and comma signs from the Amount in USD column 
Data_2020["Amount in USD"] = Data_2020["Amount in USD"].replace({"\$": "", ",": ""}, regex = True)


# Changing Amount in USD datatype to int/float 
Data_2020["Amount in USD"] = pd.to_numeric(Data_2020["Amount in USD"], errors = "coerce").fillna(0, downcast = "infer")


# Changing undisclosed values in 'Amount in USD' column to 0
Updated = Data_2020["Amount in USD"] == "Undisclosed"
Data_2020.loc[Updated, "Amount in USD"] = 0
Data_2020


# Viewing missing data in 2020 Data set
Data_2020.isnull().sum()

Company/Brand      0
Founded          212
HeadQuarter       94
Sector            13
Investor          38
Amount in USD      0
Funding Year       0
dtype: int64


Data_2020["Founded"].replace(np.NaN, value = "unknown", inplace = True)
Data_2020["HeadQuarter"].replace(np.NaN, value = "unknown", inplace = True)
Data_2020["Sector"].replace(np.NaN, value = "unknown", inplace = True)
Data_2020["Investor"].replace(np.NaN, value = "unknown", inplace = True)


# Checking for null values in dataset. 
Data_2020.isnull().sum()

Company/Brand    0
Founded          0
HeadQuarter      0
Sector           0
Investor         0
Amount in USD    0
Funding Year     0
dtype: int64


Data_2020


# Loading and cleaning 2021 Data set 
Data_2021.head()


Data_2021.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1209 entries, 0 to 1208
Data columns (total 10 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Company/Brand  1209 non-null   object 
 1   Founded        1208 non-null   float64
 2   HeadQuarter    1208 non-null   object 
 3   Sector         1209 non-null   object 
 4   What it does   1209 non-null   object 
 5   Founders       1205 non-null   object 
 6   Investor       1147 non-null   object 
 7   Amount($)      1206 non-null   object 
 8   Stage          781 non-null    object 
 9   Funding Year   1209 non-null   int64  
dtypes: float64(1), int64(1), object(8)
memory usage: 94.6+ KB


# Dropping the Stage, Founders and What it does column
Data_2021.drop(columns = ["Founders", "Stage", "What it does"], inplace = True)


#Renaming Column name Amount($) to Amount in USD 
Data_2021.rename(columns = {"Amount($)": "Amount in USD"}, inplace = True)


# Removing dollar sign from the Amount column 
Data_2021["Amount in USD"] = Data_2021["Amount in USD"].replace({"\$": "", ",": ""}, regex = True)


# Changing amount datatype to int/float 
Data_2021["Amount in USD"] = pd.to_numeric(Data_2021["Amount in USD"], errors = "coerce").fillna(0, downcast = "infer")


# Replacing columns within null values with Unknown.
Data_2021["Founded"].replace(np.NaN, value = "unknown", inplace = True)
Data_2021["HeadQuarter"].replace(np.NaN, value = "unknown", inplace = True)
Data_2021["Investor"].replace(np.NaN, value = "unknown", inplace = True)


#Changing datatype for Founded Column from object to int
Data_2021['Founded']= Data_2021['Founded'].astype('str')


Data_2021.head()


# Checking for null values in dataset. 
Data_2021.isnull().sum()

Company/Brand    0
Founded          0
HeadQuarter      0
Sector           0
Investor         0
Amount in USD    0
Funding Year     0
dtype: int64


Data_2021


Columns = [Data_2018, Data_2019, Data_2020, Data_2021]
Merged_Data = pd.concat(Columns)
Merged_Data


# Dropping the Founded and Investor columns 
Merged_Data.drop(columns = ["Founded", "Investor"], inplace = True)
Merged_Data


# dropping duplicates in the merged data set. 
Merged_Data.drop_duplicates(inplace = True)
Merged_Data


Merged_Data["Funding Year"] = pd.to_numeric(Merged_Data["Funding Year"], errors = "coerce").fillna(0, downcast = "infer")
Merged_Data


# Grouping values in the sector column after duplicates were dropped. 
group_by_sector = Merged_Data["Sector"].value_counts()
group_by_sector.head(60)

FinTech                              170
EdTech                               146
Financial Services                    86
Fintech                               84
E-commerce                            73
Edtech                                72
Automotive                            52
AgriTech                              43
Food & Beverages                      38
Logistics                             37
Gaming                                35
Healthcare                            35
Information Technology & Services     34
Healthtech                            32
HealthCare                            31
—                                     30
SaaS                                  30
SaaS startup                          28
Consumer Goods                        28
Media                                 27
HealthTech                            27
Finance                               27
Health Care                           26
Retail                                25
Tech Startup                          25
E-learning                            24
Ecommerce                             23
AI startup                            22
E-Commerce                            22
Hospitality                           22
Apps                                  20
Computer Software                     20
AI                                    20
Agritech                              19
Tech                                  19
Fashion                               19
Information Technology                19
unknown                               18
Health, Wellness & Fitness            17
Internet                              17
B2B                                   16
Logistics & Supply Chain              16
Food and Beverage                     16
Entertainment                         16
Education                             15
Food                                  15
Artificial Intelligence               15
Foodtech                              14
Real Estate                           13
Fitness                               13
Biotechnology                         13
Cosmetics                             13
Hospital & Health Care                12
Computer software                     11
Banking                               11
Automobile                            11
E-Learning                            11
Transportation                        11
IoT                                   11
Tech company                          10
Name: Sector, dtype: int64


group_by_sector

FinTech                             170
EdTech                              146
Financial Services                   86
Fintech                              84
E-commerce                           73
                                   ... 
Startup laboratory                    1
Online credit management startup      1
Battery design                        1
Eyeglasses                            1
Staffing & Recruiting                 1
Name: Sector, Length: 596, dtype: int64


# Replacing duplicate sector names in Sector column with a single name
Merged_Data.replace({'Sector':{'EdTech':'Edtech','FinTech':'Fintech','HealthCare':'Healthcare','SaaS startup':'SaaS','HealthTech': 'Healthtech', 'Ecommerce': 'E-commerce','Food':'Foodtech','AI startup':'AI','AgriTech':'Agritech','Logistics & Supply Chain':'Logistics','IT':'Information Technology','Automobile':'Automotive','Tech':'Tech Startup'}}, inplace = True)
Merged_Data


group_by_sector = Merged_Data["Sector"].value_counts()
group_by_sector

Fintech                             254
Edtech                              218
E-commerce                           96
Financial Services                   86
Healthcare                           66
                                   ... 
Online credit management startup      1
Battery design                        1
Eyeglasses                            1
Online financial service              1
Staffing & Recruiting                 1
Name: Sector, Length: 583, dtype: int64


x = Merged_Data['Company/Brand'].value_counts()[:10].index
y = Merged_Data['Company/Brand'].value_counts()[:10].values
plt.bar(x,y)
plt.rcParams['figure.figsize'] = (20,10)
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.title('TOP FUNDED START-UPS IN INDIA', fontsize = 19, fontweight = 'bold')
plt.xlabel('Start-ups', fontsize = 19, fontweight = 'bold')
plt.ylabel('Amount received', fontsize = 19, fontweight = 'bold')

Text(0, 0.5, 'Amount received')


fig1 = go.Figure(
    data=go.Pie(values=Merged_Data['HeadQuarter'].value_counts()[:8].values,labels=Merged_Data['HeadQuarter'].value_counts()[:8].index,title='Percentage of cities in every sector'))
fig1.show()


c = list(Merged_Data.groupby(Merged_Data['Funding Year']).sum()['Amount in USD'])
d = list(Merged_Data['Funding Year'].value_counts().index.sort_values())
sns.scatterplot(d,c)
plt.plot(d,c)
plt.xlabel('Year', fontsize = 22, fontweight = 'bold')
plt.ylabel('Amount(USD) in billions', fontsize = 22, fontweight = 'bold')
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.title('Amount Funded over the Years', fontsize = 22, fontweight = 'bold')
plt.rcParams['figure.figsize'] = (20,10)
warnings.filterwarnings('ignore')


sns.countplot(Merged_Data['Funding Year'])
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.xlabel('Year', fontsize = 22, fontweight = 'bold')
plt.ylabel('No. of Startups Funded', fontsize = 22, fontweight = 'bold')
plt.title('No of Startups Funded Over the Years', fontsize = 22, fontweight = 'bold')
plt.rcParams['figure.figsize'] = (18,8)
warnings.filterwarnings('ignore')


plt.figure(figsize = (25,18))
sns.barplot(y = group_by_sector[:10].index, x = group_by_sector[:10].values)
plt.xticks(fontsize = 17)
plt.yticks(fontsize = 17)
plt.xlabel("Number of Investors", fontsize = 24, fontweight = 'bold')
plt.ylabel("Sector", fontsize = 24, fontweight = 'bold')
plt.title("SECTORS PREFERRED BY INVESTORS", fontsize = 24, fontweight = 'bold')

Text(0.5, 1.0, 'SECTORS PREFERRED BY INVESTORS')

	Company Name	Industry	Round/Series	Amount	Location	About Company	Calculation	Amount.1	Funding Year
0	TheCollegeFever	Brand Marketing, Event Promotion, Marketing, S...	Seed	250000	Bangalore, Karnataka, India	TheCollegeFever is a hub for fun, fiesta and f...	0.0157	NaN	2018
1	Happy Cow Dairy	Agriculture, Farming	Seed	628000	Mumbai, Maharashtra, India	A startup which aggregates milk from dairy far...	NaN	40000000	2018
2	MyLoanCare	Credit, Financial Services, Lending, Marketplace	Series A	1020500.0	Gurgaon, Haryana, India	Leading Online Loans Marketplace in India	NaN	65000000	2018
3	PayMe India	Financial Services, FinTech	Angel	2000000	Noida, Uttar Pradesh, India	PayMe India is an innovative FinTech organizat...	NaN	NaN	2018
4	Eunimart	E-Commerce Platforms, Retail, SaaS	Seed	—	Hyderabad, Andhra Pradesh, India	Eunimart is a one stop solution for merchants ...	NaN	—	2018

	Company/Brand	Sector	Stage	Amount($)	HeadQuarter	What it does	Calculation	Amount.1	Funding Year
0	TheCollegeFever	Brand Marketing, Event Promotion, Marketing, S...	Seed	250000	Bangalore, Karnataka, India	TheCollegeFever is a hub for fun, fiesta and f...	0.0157	NaN	2018
1	Happy Cow Dairy	Agriculture, Farming	Seed	628000	Mumbai, Maharashtra, India	A startup which aggregates milk from dairy far...	NaN	40000000	2018
2	MyLoanCare	Credit, Financial Services, Lending, Marketplace	Series A	1020500.0	Gurgaon, Haryana, India	Leading Online Loans Marketplace in India	NaN	65000000	2018
3	PayMe India	Financial Services, FinTech	Angel	2000000	Noida, Uttar Pradesh, India	PayMe India is an innovative FinTech organizat...	NaN	NaN	2018
4	Eunimart	E-Commerce Platforms, Retail, SaaS	Seed	—	Hyderabad, Andhra Pradesh, India	Eunimart is a one stop solution for merchants ...	NaN	—	2018

	Company/Brand	Sector	Amount($)	HeadQuarter	Funding Year
0	TheCollegeFever	Brand Marketing, Event Promotion, Marketing, S...	250000	Bangalore, Karnataka, India	2018
1	Happy Cow Dairy	Agriculture, Farming	628000	Mumbai, Maharashtra, India	2018
2	MyLoanCare	Credit, Financial Services, Lending, Marketplace	1020500.0	Gurgaon, Haryana, India	2018
3	PayMe India	Financial Services, FinTech	2000000	Noida, Uttar Pradesh, India	2018
4	Eunimart	E-Commerce Platforms, Retail, SaaS	—	Hyderabad, Andhra Pradesh, India	2018

	Company/Brand	Sector	Amount in USD	HeadQuarter	Funding Year
0	TheCollegeFever	Brand Marketing	250000.0	Bangalore	2018
1	Happy Cow Dairy	Agriculture	628000.0	Mumbai	2018
2	MyLoanCare	Credit	1020500.0	Gurgaon	2018
3	PayMe India	Financial Services	2000000.0	Noida	2018
4	Eunimart	E-Commerce Platforms	0.0	Hyderabad	2018
...	...	...	...	...	...
521	Udaan	B2B	225000000.0	Bangalore	2018
522	Happyeasygo Group	Tourism	0.0	Haryana	2018
523	Mombay	Food and Beverage	7500.0	Mumbai	2018
524	Droni Tech	Information Technology	549500.0	Mumbai	2018
525	Netmeds	Biotechnology	35000000.0	Chennai	2018

	Company/Brand	Sector	Amount in USD	HeadQuarter	Funding Year
0	TheCollegeFever	Brand Marketing	250000.0	Bangalore	2018
1	Happy Cow Dairy	Agriculture	628000.0	Mumbai	2018
2	MyLoanCare	Credit	1020500.0	Gurgaon	2018
3	PayMe India	Financial Services	2000000.0	Noida	2018
4	Eunimart	E-Commerce Platforms	0.0	Hyderabad	2018
...	...	...	...	...	...
521	Udaan	B2B	225000000.0	Bangalore	2018
522	Happyeasygo Group	Tourism	0.0	Haryana	2018
523	Mombay	Food and Beverage	7500.0	Mumbai	2018
524	Droni Tech	Information Technology	549500.0	Mumbai	2018
525	Netmeds	Biotechnology	35000000.0	Chennai	2018

	Company/Brand	Founded	HeadQuarter	Sector	What it does	Founders	Investor	Amount($)	Stage	Funding Year
0	Bombay Shaving	NaN	NaN	Ecommerce	Provides a range of male grooming products	Shantanu Deshpande	Sixth Sense Ventures	$6,300,000	NaN	2019
1	Ruangguru	2014.0	Mumbai	Edtech	A learning platform that provides topic-based ...	Adamas Belva Syah Devara, Iman Usman.	General Atlantic	$150,000,000	Series C	2019
2	Eduisfun	NaN	Mumbai	Edtech	It aims to make learning fun via games.	Jatin Solanki	Deepak Parekh, Amitabh Bachchan, Piyush Pandey	$28,000,000	Fresh funding	2019
3	HomeLane	2014.0	Chennai	Interior design	Provides interior designing solutions	Srikanth Iyer, Rama Harinath	Evolvence India Fund (EIF), Pidilite Group, FJ...	$30,000,000	Series D	2019
4	Nu Genes	2004.0	Telangana	AgriTech	It is a seed company engaged in production, pr...	Narayana Reddy Punyala	Innovation in Food and Agriculture (IFA)	$6,000,000	NaN	2019

	Company/Brand	Founded	HeadQuarter	Sector	Investor	Amount in USD	Funding Year
0	Bombay Shaving	unknown	unknown	Ecommerce	Sixth Sense Ventures	6300000	2019
1	Ruangguru	2014.0	Mumbai	Edtech	General Atlantic	150000000	2019
2	Eduisfun	unknown	Mumbai	Edtech	Deepak Parekh, Amitabh Bachchan, Piyush Pandey	28000000	2019
3	HomeLane	2014.0	Chennai	Interior design	Evolvence India Fund (EIF), Pidilite Group, FJ...	30000000	2019
4	Nu Genes	2004.0	Telangana	AgriTech	Innovation in Food and Agriculture (IFA)	6000000	2019
...	...	...	...	...	...	...	...
84	Infra.Market	unknown	Mumbai	Infratech	Tiger Global, Nexus Venture Partners, Accel Pa...	20000000	2019
85	Oyo	2013.0	Gurugram	Hospitality	MyPreferred Transformation, Avendus Finance, S...	693000000	2019
86	GoMechanic	2016.0	Delhi	Automobile & Technology	Sequoia Capital	5000000	2019
87	Spinny	2015.0	Delhi	Automobile	Norwest Venture Partners, General Catalyst, Fu...	50000000	2019
88	Ess Kay Fincorp	unknown	Rajasthan	Banking	TPG, Norwest Venture Partners, Evolvence India	33000000	2019

	Company/Brand	Founded	HeadQuarter	Sector	What it does	Founders	Investor	Amount($)	Stage	Unnamed: 9	Funding Year
0	Aqgromalin	2019	Chennai	AgriTech	Cultivating Ideas for Profit	Prasanna Manogaran, Bharani C L	Angel investors	$200,000	NaN	NaN	2020
1	Krayonnz	2019	Bangalore	EdTech	An academy-guardian-scholar centric ecosystem ...	Saurabh Dixit, Gurudutt Upadhyay	GSF Accelerator	$100,000	Pre-seed	NaN	2020
2	PadCare Labs	2018	Pune	Hygiene management	Converting bio-hazardous waste to harmless waste	Ajinkya Dhariya	Venture Center	Undisclosed	Pre-seed	NaN	2020
3	NCOME	2020	New Delhi	Escrow	Escrow-as-a-service platform	Ritesh Tiwari	Venture Catalysts, PointOne Capital	$400,000	NaN	NaN	2020
4	Gramophone	2016	Indore	AgriTech	Gramophone is an AgTech platform enabling acce...	Ashish Rajan Singh, Harshit Gupta, Nishant Mah...	Siana Capital Management, Info Edge	$340,000	NaN	NaN	2020

	Company/Brand	Founded	HeadQuarter	Sector	What it does	Founders	Investor	Amount($)	Stage	Funding Year
0	Unbox Robotics	2019.0	Bangalore	AI startup	Unbox Robotics builds on-demand AI-driven ware...	Pramod Ghadge, Shahid Memon	BEENEXT, Entrepreneur First	$1,200,000	Pre-series A	2021
1	upGrad	2015.0	Mumbai	EdTech	UpGrad is an online higher education platform.	Mayank Kumar, Phalgun Kompalli, Ravijot Chugh,...	Unilazer Ventures, IIFL Asset Management	$120,000,000	NaN	2021
2	Lead School	2012.0	Mumbai	EdTech	LEAD School offers technology based school tra...	Smita Deorah, Sumeet Mehta	GSV Ventures, Westbridge Capital	$30,000,000	Series D	2021
3	Bizongo	2015.0	Mumbai	B2B E-commerce	Bizongo is a business-to-business online marke...	Aniket Deb, Ankit Tomar, Sachin Agrawal	CDC Group, IDG Capital	$51,000,000	Series C	2021
4	FypMoney	2021.0	Gurugram	FinTech	FypMoney is Digital NEO Bank for Teenagers, em...	Kapil Banwari	Liberatha Kallat, Mukesh Yadav, Dinesh Nagpal	$2,000,000	Seed	2021

INDIAN START UP FUNDING PROJECT LP1¶

The goal of this project is to analyze the funding received by startups in India from 2018 to 2021. The data for each year of funding is found in separate csv files. These files are what I’m going to use in asking my questions, stating my hypothesis, cleaning my data and analyzing my data.¶

QUESTIONS¶

1. Who are the top funded start-ups in India?¶

2. Which cities are most favoured by investors?¶

3. How does the funding change in time?¶

4. Which year records the most start-ups funded?¶

5. Which sectors do investors prefer?¶

HYPOTHESIS¶

1. Investors prefer to invest in Tech start-ups more than the other start ups¶

2. The location of a start-up is a major factor in receiving funds from Investors.¶

Importing Libraries¶

Since I will be merging datasets at the end of the cleaning process, I have decided to add a new column called "Funding Year" to all datasets, to distinguish data after merging.¶

Loading and Cleaning Datasets individually before merging¶

Upon observation, the 2018 dataset has different column names from the rest of the datasets. Since I will be merging the data in the end, there must be uniformity in column names. Hence I am going to rename the column names in the 2018 dataset to suit that of the others.¶

Looking at the 2018 dataset above, the amount column has a datatype in the form of a string. The amount column is supposed to be in an Int or Float datatype, so I went ahead to change the datatype from object to int.¶

The Sector and Headquarter columns in the 2018 dataset has multiple sector names. I am going to strip off the commas and select just the first names of Sectors and Headquarters, to make my analysis and visualization clean and concise.¶

Cleaned 2018 Dataset¶

Loading and Cleaning 2019 Dataset¶

Cleaned 2019 Dataset¶

Loading and Cleaning 2020 Dataset¶

I am going to replace all null values in the Founded, Sector and Investor columns with unknown. I did not go the extra mile to look for missing values because its a bit irrelevant to my questions.¶

Cleaned 2020 Dataset¶

Loading and Cleaning 2021 dataset¶

CLEANED 2021 DATA¶

Merging 2018,2019,2020 and 2021 datasets for Analysis and Visualization¶

DATA ANALYSIS AND VISUALIZATION¶

The libraries I am going to use for my analysis and visualization have already been imported at the start of my data cleaning. They are matplotlib, seaborn, and plotly.¶

Q1. WHO ARE THE TOP FUNDED STARTUPS IN INDIA?¶

From the chart, we can see that BharatPe is the top funded startup in the Indian Ecosystem. Bharat is a fintech start up founded in 2018 and has it's HeadQuarter located in Delhi, India. Zomato falls next in place as the next top funded start-up in India.¶

Q2. Which cities are the most favoured by Investors?¶

For this analysis, I am going to use a pie chart to demonstrate the cities that are most favored by investors.¶

Q3 . How does the funding change in time?¶

Q4. Which year records the most start-ups funded?¶

From the graph above, it is clear that 2021 received the most start up funding. 2019 recorded the least funding.¶

Q5. Which Sectors do Investors prefer?¶

Summary¶

1. The top funded start ups in the Indian Start-up ecosystem are BharatePe, Zomato and Zetwerk¶

2. Bangalore, known as the Silicon Valley State of India, is the most preferred city by Investors. Followed by Mumbai.¶

3. 2021 records the year with the most start ups funded¶

4. Investors prefer to invest more in Tech start-up companies.¶

Conclusion¶

To satisfy my hypothesis,¶

1. Investors prefer to invest in Tech start-ups more than the other start ups.¶

This is clearly stated in Fig 5. Investors prefer to invest in Tech start-ups. Looking at the chart, more than 3 start ups are from the tech sector, with Fintech being at the top, followed by Edtech.¶

2. The Location of a start-up plays a major role in receiving funds from Investors¶

Yes they do. According to this survey start-ups in Bangalore alone have got more funding than the next 6 cities in line put together. This could also be owing to the fact that Bangalore has the most number of start-ups in the country.¶